Function to visualise a supra-hexagonal grid

Description

visHexGrid is supposed to visualise a supra-hexagonal grid

Usage

visHexGrid(hbin, area.size = 1, border.color = NULL, fill.color = NULL, lty = 1, 
  lwd = 1, lineend = "round", linejoin = "round")

Arguments

hbin
an object of class "hexbin"
area.size
an inteter or a vector specifying the area size of each hexagon
border.color
the border color for each hexagon
fill.color
the filled color for each hexagon
lty
the line type for each hexagon. 0 for 'blank', 1 for 'solid', 2 for 'dashed', 3 for 'dotted', 4 for 'dotdash', 5 for 'longdash', 6 for 'twodash'
lwd
the line width for each hexagon
lineend
the line end style for each hexagon. It can be one of 'round', 'butt' and 'square'
linejoin
the line join style for each hexagon. It can be one of 'round', 'mitre' and 'bevel'

Value

invisible

Note

none

Examples

# 1) generate an iid normal random matrix of 100x10 data <- matrix( rnorm(100*10,mean=0,sd=1), nrow=100, ncol=10) colnames(data) <- paste(rep('S',10), seq(1:10), sep="") # 2) sMap resulted from using by default setup sMap <- sPipeline(data=data)
Start at 2017-03-27 18:55:26 First, define topology of a map grid (2017-03-27 18:55:26)... Second, initialise the codebook matrix (61 X 10) using 'linear' initialisation, given a topology and input data (2017-03-27 18:55:26)... Third, get training at the rough stage (2017-03-27 18:55:26)... 1 out of 7 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 2 out of 7 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 3 out of 7 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 4 out of 7 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 5 out of 7 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 6 out of 7 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 7 out of 7 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) Fourth, get training at the finetune stage (2017-03-27 18:55:26)... 1 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 2 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 3 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 4 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 5 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 6 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 7 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 8 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 9 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 10 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 11 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 12 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 13 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 14 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 15 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 16 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 17 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 18 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 19 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 20 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 21 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 22 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 23 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 24 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) 25 out of 25 (2017-03-27 18:55:26) updated (2017-03-27 18:55:26) Next, identify the best-matching hexagon/rectangle for the input data (2017-03-27 18:55:26)... Finally, append the response data (hits and mqe) into the sMap object (2017-03-27 18:55:26)... Below are the summaries of the training results: dimension of input data: 100x10 xy-dimension of map grid: xdim=9, ydim=9, r=5 grid lattice: hexa grid shape: suprahex dimension of grid coord: 61x2 initialisation method: linear dimension of codebook matrix: 61x10 mean quantization error: 4.9630432442287 Below are the details of trainology: training algorithm: batch alpha type: invert training neighborhood kernel: gaussian trainlength (x input data length): 7 at rough stage; 25 at finetune stage radius (at rough stage): from 3 to 1 radius (at finetune stage): from 1 to 1 End at 2017-03-27 18:55:26 Runtime in total is: 0 secs
# 3) create an object of "hexbin" class from sMap dat <- data.frame(sMap$coord) xdim <- sMap$xdim ydim <- sMap$ydim hbin <- hexbin::hexbin(dat$x, dat$y, xbins=xdim-1, shape=sqrt(0.75)*ydim/xdim) # 4) visualise hbin object vp <- hexbin::hexViewport(hbin) visHexGrid(hbin)

Source code

visHexGrid.r

Source man

visHexGrid.Rd visHexGrid.pdf

See also

visHexComp